We take the first steps towards answering these questions in the present article by looking at human exploration from a resource-rational perspective [1]}, [2]}, [3]}. More specifically, we investigate the hypothesis that people solve the exploration-exploitation dilemma by making optimal use of limited computational resources. To test this hypothesis, we devise a family of resource-rational reinforcement learning algorithms by combining ideas from meta-learning [4]}, [5]} and information theory [6]}. The resulting model – which we call RL\(^3\)  – implements a free-standing reinforcement learning algorithm that achieves optimal behavior subject to the constraint that it can be implemented with a given number of bits.
